Data Engineer Client Data Operations

Limetree is hiring a hands-on Data Engineer to onboard client data and build reliable pipelines that support direct mail, digital marketing, and reporting. The role owns production monitoring and troubleshooting, routine mail-drop execution, and delivery of clean, documented data to analytics teams.

Responsibilities:

• Set up new feeds from client and vendor systems, including SFTP and scheduled file deliveries; identify source fields, changes, missing data, and inconsistent formats
• Map incoming data into established schemas and tables, identify sensitive fields, and apply approved privacy and access rules. Raise ambiguous data or policy questions before moving forward
• Build the validation and matching steps that make data usable, including record counts, identity matching or LTID assignment, and CASS/NCOA processing where relevant
• Document each feed so someone else can understand the inputs, transformations, checks, outputs, and what to do when it fails
• Build and extend SQL, Python, and Spark pipelines as we bring on new direct mail and digital clients, using consistent data structures rather than one-off solutions
• Set up scheduled jobs, file watchers, transformations, testing, and monitoring so new data streams run with minimal manual intervention
• Improve existing pipelines as source files, schemas, tools, and business requirements change
• Own the day-to-day checks and troubleshooting for current Databricks workflows, AWS jobs, and related tools; investigate failures and fix root causes
• Handle schema drift, bad source files, expired credentials, broken dependencies, stale data, and vendor-tool issues; improve alerts, recovery steps, and runbooks
• Take ownership of routine audience and mail-drop production, including suppressions, address hygiene, count reconciliation, QC, and vendor file preparation, within established approvals
• Flag risks to scheduled client drops early, explain what is affected, and recommend a safe path to resolution
• Hand off reliable, documented data to our Senior Analytics Engineer for downstream modeling, analysis, and reporting; work together when definitions or matching rules need decisions
• Partner with campaign, account, and technical colleagues to clarify requirements and resolve issues without making them chase the status of a feed or delivery

Requirements:

• Several years of hands-on data engineering experience, ideally four or more, including pipelines you have operated in production and failures you personally diagnosed
• Comfort taking a messy or poorly documented source file from first inspection through a reliable production data feed
• Strong attention to detail: you reconcile counts, question suspicious data, and make fixes that prevent a problem from coming back
• Able to work independently, explain technical issues plainly, and know when a business rule or privacy question needs an owner to decide
• Strong SQL and Python; practical experience with Spark or similar large-scale data processing
• Production cloud data engineering experience. We currently use Databricks and AWS, but value strong AWS-native or comparable experience as our platform evolves
• Experience with orchestration, monitoring, schema mapping, file-based integrations, access controls, Git, testing, and operational documentation
• Careful handling of personally identifiable or protected health information in regulated data environments
• Direct mail and/or digital marketing audience data, including deduplication, suppressions, CASS/NCOA, AccuZIP, AccuTrace, USPS tracking, or digital marketing sources and APIs
• Healthcare, Medicare, or health plan marketing data; SFTP, S3, Parquet, and client-facing reporting feeds